OpenRaiser/Intern-Atlas huggingface dataset:
Label driven Knowledge Distillation for Federated Learning with non-IID Data
Venue: arXiv cs.LG 2022
Year: 2022

In real-world applications, Federated Learning (FL) meets two challenges: (1) scalability, especially when applied to massive IoT networks; and (2) how to be robust against an environment with heterogeneous data. Realizing the first problem, we aim to design a novel FL framework named Full-stack FL (F2L). More specifically, F2L utilizes a hierarchical network architecture, making extending the FL network accessible without reconstructing the whole network system. Moreover, leveraging the advantages of hierarchical network design, we propose a new label-driven knowledge distillation (LKD) technique at the global server to address the second problem. As opposed to current knowledge distillation techniques, LKD is capable of training a student model, which consists of good knowledge from all teachers' models. Therefore, our proposed algorithm can effectively extract the knowledge of the regions' data distribution (i.e., the regional aggregated models) to reduce the divergence between clients' models when operating under the FL system with non-independent identically distributed data. Extensive experiment results reveal that: (i) our F2L method can significantly improve the overall FL efficiency in all global distillations, and (ii) F2L rapidly achieves convergence as global distillation stages occur instead of increasing on each communication cycle.
Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applications
Venue: arXiv cs.CV 2022
Year: 2022

Guaranteeing real-time and accurate object detection simultaneously is paramount in autonomous driving environments. However, the existing object detection neural network systems are characterized by a tradeoff between computation time and accuracy, making it essential to optimize such a tradeoff. Fortunately, in many autonomous driving environments, images come in a continuous form, providing an opportunity to use optical flow. In this paper, we improve the performance of an object detection neural network utilizing optical flow estimation. In addition, we propose a Lyapunov optimization framework for time-average performance maximization subject to stability. It adaptively determines whether to use optical flow to suit the dynamic vehicle environment, thereby ensuring the vehicle's queue stability and the time-average maximum performance simultaneously. To verify the key ideas, we conduct numerical experiments with various object detection neural networks and optical flow estimation networks. In addition, we demonstrate the self-configurable stabilized detection with YOLOv3-tiny and FlowNet2-S, which are the real-time object detection network and an optical flow estimation network, respectively. In the demonstration, our proposed framework improves the accuracy by 3.02%, the number of detected objects by 59.6%, and the queue stability for computing capabilities.
Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Experts
Venue: arXiv cs.CL 2022
Year: 2022

Media coverage has a substantial effect on the public perception of events. Nevertheless, media outlets are often biased. One way to bias news articles is by altering the word choice. The automatic identification of bias by word choice is challenging, primarily due to the lack of a gold standard data set and high context dependencies. This paper presents BABE, a robust and diverse data set created by trained experts, for media bias research. We also analyze why expert labeling is essential within this domain. Our data set offers better annotation quality and higher inter-annotator agreement than existing work. It consists of 3,700 sentences balanced among topics and outlets, containing media bias labels on the word and sentence level. Based on our data, we also introduce a way to detect bias-inducing sentences in news articles automatically. Our best performing BERT-based model is pre-trained on a larger corpus consisting of distant labels. Fine-tuning and evaluating the model on our proposed supervised data set, we achieve a macro F1-score of 0.804, outperforming existing methods.
Scaling transformation of the multimode nonlinear Schrödinger equation for physics-informed neural networks
Venue: arXiv cs.NE 2022
Year: 2022

Single-mode optical fibers (SMFs) have become the backbone of modern communication systems. However, their throughput is expected to reach its theoretical limit in the nearest future. Utilization of multimode fibers (MMFs) is considered as one of the most promising solutions rectifying this capacity crunch. Nevertheless, differential equations describing light propagation in MMFs are a way more sophisticated than those for SMFs, which makes numerical modelling of MMF-based systems computationally demanding and impractical for the most part of realistic scenarios. Physics-informed neural networks (PINNs) are known to outperform conventional numerical approaches in various domains and have been successfully applied to the nonlinear Schrödinger equation (NLSE) describing light propagation in SMFs. A comprehensive study on application of PINN to the multimode NLSE (MMNLSE) is still lacking though. To the best of our knowledge, this paper is the first to deploy the paradigm of PINN for MMNLSE and to demonstrate that a straightforward implementation of PINNs by analogy with NLSE does not work out. We pinpoint all issues hindering PINN convergence and introduce a novel scaling transformation for the zero-order dispersion coefficient that makes PINN capture all relevant physical effects. Our simulations reveal good agreement with the split-step Fourier (SSF) method and extend numerically attainable propagation lengths up to several hundred meters. All major limitations are also highlighted.
Bounded Future MS-TCN++ for surgical gesture recognition
Venue: arXiv cs.CV 2022
Year: 2022

In recent times there is a growing development of video based applications for surgical purposes. Part of these applications can work offline after the end of the procedure, other applications must react immediately. However, there are cases where the response should be done during the procedure but some delay is acceptable. In the literature, the online-offline performance gap is known. Our goal in this study was to learn the performance-delay trade-off and design an MS-TCN++-based algorithm that can utilize this trade-off. To this aim, we used our open surgery simulation data-set containing 96 videos of 24 participants that perform a suturing task on a variable tissue simulator. In this study, we used video data captured from the side view. The Networks were trained to identify the performed surgical gestures. The naive approach is to reduce the MS-TCN++ depth, as a result, the receptive field is reduced, and also the number of required future frames is also reduced. We showed that this method is sub-optimal, mainly in the small delay cases. The second method was to limit the accessible future in each temporal convolution. This way, we have flexibility in the network design and as a result, we achieve significantly better performance than in the naive approach.
Chameleon Cache: Approximating Fully Associative Caches with Random Replacement to Prevent Contention-Based Cache Attacks
Venue: arXiv cs.CR 2022
Year: 2022

Randomized, skewed caches (RSCs) such as CEASER-S have recently received much attention to defend against contention-based cache side channels. By randomizing and regularly changing the mapping(s) of addresses to cache sets, these techniques are designed to obfuscate the leakage of memory access patterns. However, new attack techniques, e.g., Prime+Prune+Probe, soon demonstrated the limits of RSCs as they allow attackers to more quickly learn which addresses contend in the cache and use this information to circumvent the randomization. To yet maintain side-channel resilience, RSCs must change the random mapping(s) more frequently with adverse effects on performance and implementation complexity. This work aims to make randomization-based approaches more robust to allow for reduced re-keying rates and presents Chameleon Cache. Chameleon Cache extends RSCs with a victim cache (VC) to decouple contention in the RSC from evictions observed by the user. The VC allows Chameleon Cache to make additional use of the multiple mappings RSCs provide to translate addresses to cache set indices: when a cache line is evicted from the RSC to the VC under one of its mappings, the VC automatically reinserts this evicted line back into the RSC by using a different mapping. As a result, the effects of previous RSC set contention are hidden and Chameleon Cache exhibits side-channel resistance and eviction patterns similar to fully associative caches with random replacement. We show that Chameleon Cache has performance overheads of < 1% and stress that VCs are more generically helpful to increase side-channel resistance and re-keying intervals of randomized caches.
Self-stabilizing Total-order Broadcast
Venue: arXiv cs.DC 2022
Year: 2022

The problem of total-order (uniform reliable) broadcast is fundamental in fault-tolerant distributed computing since it abstracts a broad set of problems requiring processes to uniformly deliver messages in the same order in which they were sent. Existing solutions (that tolerate process failures) reduce the total-order broadcast problem to the one of multivalued consensus. Our study aims at the design of an even more reliable solution. We do so through the lenses of self-stabilization-a very strong notion of fault tolerance. In addition to node and communication failures, self-stabilizing algorithms can recover after the occurrence of arbitrary transient faults; these faults represent any violation of the assumptions according to which the system was designed to operate (as long as the algorithm code stays intact). This work proposes the first (to the best of our knowledge) self-stabilizing algorithm for total-order (uniform reliable) broadcast for asynchronous message-passing systems prone to process failures and transient faults. As we show, the proposed solution facilitates the elegant construction of self-stabilizing state-machine replication using bounded memory.
GROOT: Corrective Reward Optimization for Generative Sequential Labeling
Venue: arXiv cs.CL 2022
Year: 2022

Sequential labeling is a fundamental NLP task, forming the backbone of many applications. Supervised learning of Seq2Seq models has shown great success on these problems. However, the training objectives are still significantly disconnected with the metrics and desiderata we care about in practice. For example, a practical sequence tagging application may want to optimize for a certain precision-recall trade-off (of the top-k predictions) which is quite different from the standard objective of maximizing the likelihood of the gold labeled sequence. Thus to bridge this gap, we propose GROOT -- a simple yet effective framework for Generative Reward Optimization Of Text sequences. GROOT works by training a generative sequential labeling model to match the decoder output distribution with that of the (black-box) reward function. Using an iterative training regime, we first generate prediction candidates, then correct errors in them, and finally contrast those candidates (based on their reward values). As demonstrated via extensive experiments on four public benchmarks, GROOT significantly improves all reward metrics. Furthermore, GROOT leads to improvements of the overall decoder distribution as evidenced by the quality gains of the top-$k$ candidates.
Evolving Reference Architecture Description: Guidelines based on ISO/IEC/IEEE 42010
Venue: arXiv cs.SE 2022
Year: 2022

The architectural design of software systems is not a trivial task, requiring sometimes large experience and knowledge accumulated for years. Reference architectures have been increasingly adopted as a means to support such task, also contributing to the standardization and evolution of these systems. Although considerable time and effort are devoted to design these architectures, an outdated description is still found in several of them and, as a consequence, resulting in their non-continuation. This article presents guidelines to evolve the description of reference architectures, considering different types of stakeholders and required tasks. To complement our statement that the guidelines are correct by construction as they were grounded in widely known international standard ISO/IEC/IEEE 42010 and literature, we also briefly present a qualitative analysis comparing the guidelines with an ad hoc way (commonly occurred in reference architectures). We believe solutions like these guidelines are necessary and could further contribute to the sustainability and longevity of reference architectures.
FastPacket: Towards Pre-trained Packets Embedding based on FastText for next-generation NIDS
Venue: arXiv cs.CR 2022
Year: 2022

New Attacks are increasingly used by attackers everyday but many of them are not detected by Intrusion Detection Systems as most IDS ignore raw packet information and only care about some basic statistical information extracted from PCAP files. Using networking programs to extract fixed statistical features from packets is good, but may not enough to detect nowadays challenges. We think that it is time to utilize big data and deep learning for automatic dynamic feature extraction from packets. It is time to get inspired by deep learning pre-trained models in computer vision and natural language processing, so security deep learning solutions will have its pre-trained models on big datasets to be used in future researches. In this paper, we proposed a new approach for embedding packets based on character-level embeddings, inspired by FastText success on text data. We called this approach FastPacket. Results are measured on subsets of CIC-IDS-2017 dataset, but we expect promising results on big data pre-trained models. We suggest building pre-trained FastPacket on MAWI big dataset and make it available to community, similar to FastText. To be able to outperform currently used NIDS, to start a new era of packet-level NIDS that can better detect complex attacks.
Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
Venue: arXiv cs.CV 2022
Year: 2022

The reconstruction loss and the Kullback-Leibler divergence (KLD) loss in a variational autoencoder (VAE) often play antagonistic roles, and tuning the weight of the KLD loss in $β$-VAE to achieve a balance between the two losses is a tricky and dataset-specific task. As a result, current practices in VAE training often result in a trade-off between the reconstruction fidelity and the continuity$/$disentanglement of the latent space, if the weight $β$ is not carefully tuned. In this paper, we present intuitions and a careful analysis of the antagonistic mechanism of the two losses, and propose, based on the insights, a simple yet effective two-stage method for training a VAE. Specifically, the method aggregates a learned Gaussian posterior $z \sim q_θ (z|x)$ with a decoder decoupled from the KLD loss, which is trained to learn a new conditional distribution $p_ϕ (x|z)$ of the input data $x$. Experimentally, we show that the aggregated VAE maximally satisfies the Gaussian assumption about the latent space, while still achieves a reconstruction error comparable to when the latent space is only loosely regularized by $\mathcal{N}(\mathbf{0},I)$. The proposed approach does not require hyperparameter (i.e., the KLD weight $β$) tuning given a specific dataset as required in common VAE training practices. We evaluate the method using a medical dataset intended for 3D skull reconstruction and shape completion, and the results indicate promising generative capabilities of the VAE trained using the proposed method. Besides, through guided manipulation of the latent variables, we establish a connection between existing autoencoder (AE)-based approaches and generative approaches, such as VAE, for the shape completion problem. Codes and pre-trained weights are available at https://github.com/Jianningli/skullVAE
EMF-Aware MU-MIMO Beamforming in RIS-Aided Cellular Networks
Venue: arXiv cs.IT 2022
Year: 2022

Reconfigurable Intelligent Surfaces (RISs) are one of the key emerging 6th Generation (6G) technologies that are expected to improve the link budgets between transmitters and receivers by adding artificial propagation paths. In such re-configured propagation environment, Downlink (DL) Multi-User Multi-Input Multi-Output (MU-MIMO) brings capacity improvement to cellular networks. It benefits from the spatial dimension offered by MIMO systems to enable simultaneous transmission of independent data streams to multiple users on the same radio resources by applying appropriate Beamforming (BF) schemes. However, in some cases, serving the same subset of users for a long period of time may cause some undesired regions where the average Electromagnetic Field Exposure (EMFE) exceeds the regulatory limits. To address this challenge, we propose in this paper a novel Electromagnetic Field (EMF) aware MU-MIMO BF scheme that aims to optimize the overall capacity under EMF constraints in RIS-aided cellular networks.
ThreatPro: Multi-Layer Threat Analysis in the Cloud
Venue: arXiv cs.CR 2022
Year: 2022

Many effective Threat Analysis (TA) techniques exist that focus on analyzing threats to targeted assets (e.g., components, services). These techniques consider static interconnections among the assets. However, in dynamic environments, such as the Cloud, resources can instantiate, migrate across physical hosts, or decommission to provide rapid resource elasticity to the users. It is evident that existing TA techniques cannot address all these requirements. In addition, there is an increasing number of complex multi-layer/multi-asset attacks on Cloud systems, such as the Equifax data breach. Hence, there is a need for threat analysis approaches that are designed to analyze threats in complex, dynamic, and multi-layer Cloud environments. In this paper, we propose ThreatPro that addresses the analysis of multi-layer attacks and supports dynamic interconnections in the Cloud. ThreatPro facilitates threat analysis by developing a technology-agnostic information flow model, which represents the Cloud's functionality through a set of conditional transitions. The model establishes the basis to capture the multi-layer and dynamic interconnections during the life-cycle of a Virtual Machine (VM). Specifically, ThreatPro contributes in (a) enabling the exploration of a threat's behavior and its propagation across the Cloud, and (b) assessing the security of the Cloud by analyzing the impact of multiple threats across various operational layers/assets. Using public information on threats from the National Vulnerability Database (NVD), we validate ThreatPro's capabilities, i.e., (a) identify and trace actual Cloud attacks and (b) speculatively postulate alternate potential attack paths.
Named Entity Recognition in Industrial Tables using Tabular Language Models
Venue: arXiv cs.AI 2022
Year: 2022

Specialized transformer-based models for encoding tabular data have gained interest in academia. Although tabular data is omnipresent in industry, applications of table transformers are still missing. In this paper, we study how these models can be applied to an industrial Named Entity Recognition (NER) problem where the entities are mentioned in tabular-structured spreadsheets. The highly technical nature of spreadsheets as well as the lack of labeled data present major challenges for fine-tuning transformer-based models. Therefore, we develop a dedicated table data augmentation strategy based on available domain-specific knowledge graphs. We show that this boosts performance in our low-resource scenario considerably. Further, we investigate the benefits of tabular structure as inductive bias compared to tables as linearized sequences. Our experiments confirm that a table transformer outperforms other baselines and that its tabular inductive bias is vital for convergence of transformer-based models.
Trading off Quality for Efficiency of Community Detection: An Inductive Method across Graphs
Venue: arXiv cs.SI 2022
Year: 2022

Many network applications can be formulated as NP-hard combinatorial optimization problems of community detection (CD). Due to the NP-hardness, to balance the CD quality and efficiency remains a challenge. Most existing CD methods are transductive, which are independently optimized only for the CD on a single graph. Some of these methods use advanced machine learning techniques to obtain high-quality CD results but usually have high complexity. Other approaches use fast heuristic approximation to ensure low runtime but may suffer from quality degradation. In contrast to these transductive methods, we propose an alternative inductive community detection (ICD) method across graphs of a system or scenario to alleviate the NP-hard challenge. ICD first conducts the offline training of an adversarial dual GNN on historical graphs to capture key properties of the system. The trained model is then directly generalized to new unseen graphs for online CD without additional optimization, where a better trade-off between quality and efficiency can be achieved. ICD can also capture the permutation invariant community labels in the offline training and tackle the online CD on new graphs with non-fixed number of nodes and communities. Experiments on a set of benchmarks demonstrate that ICD can achieve a significant trade-off between quality and efficiency over various baselines.
Lightweight Monocular Depth Estimation with an Edge Guided Network
Venue: arXiv cs.CV 2022
Year: 2022

Monocular depth estimation is an important task that can be applied to many robotic applications. Existing methods focus on improving depth estimation accuracy via training increasingly deeper and wider networks, however these suffer from large computational complexity. Recent studies found that edge information are important cues for convolutional neural networks (CNNs) to estimate depth. Inspired by the above observations, we present a novel lightweight Edge Guided Depth Estimation Network (EGD-Net) in this study. In particular, we start out with a lightweight encoder-decoder architecture and embed an edge guidance branch which takes as input image gradients and multi-scale feature maps from the backbone to learn the edge attention features. In order to aggregate the context information and edge attention features, we design a transformer-based feature aggregation module (TRFA). TRFA captures the long-range dependencies between the context information and edge attention features through cross-attention mechanism. We perform extensive experiments on the NYU depth v2 dataset. Experimental results show that the proposed method runs about 96 fps on a Nvidia GTX 1080 GPU whilst achieving the state-of-the-art performance in terms of accuracy.